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diff --git a/images/template/mineAP_02.png b/images/template/mineAP_02.png
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diff --git a/images/template/mineAT_01.png b/images/template/mineAT_01.png
deleted file mode 100644
index e8244f9..0000000
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diff --git a/images/template/mineAT_04.png b/images/template/mineAT_04.png
deleted file mode 100644
index d3d6ee7..0000000
Binary files a/images/template/mineAT_04.png and /dev/null differ
diff --git a/images/template/mineAtom.png b/images/template/mineAtom.png
deleted file mode 100644
index ee1f0d4..0000000
Binary files a/images/template/mineAtom.png and /dev/null differ
diff --git a/images/template/rock_01.png b/images/template/rock_01.png
deleted file mode 100644
index c940293..0000000
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diff --git a/images/template/rock_02.png b/images/template/rock_02.png
deleted file mode 100644
index 9a2688b..0000000
Binary files a/images/template/rock_02.png and /dev/null differ
diff --git a/requirements.txt b/requirements.txt
index 00d581f..b250ace 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,10 @@
 pygame==2.0.1
 anytree~=2.8.0
+anytree~=2.8.0
+numpy~=1.20.3
+torch~=1.8.1
+opencv-python~=4.5.2.52
+torchvision~=0.9.1
+pandas~=1.2.4
+torchaudio~=0.8.1
+PySoundFile~=0.9.0
\ No newline at end of file
diff --git a/src/const.py b/src/const.py
index 9c4b00e..148f92b 100644
--- a/src/const.py
+++ b/src/const.py
@@ -2,11 +2,13 @@ import os
 import pickle
 
 import pygame as pg
+import torch
 
 from image import Image
+from machine_learning.neural_network.net import Net
 
 
-main_path = os.path.dirname(os.getcwd())
+main_path = os.path.dirname(os.path.dirname(__file__))
 
 WIDTH = 800
 HEIGHT = 800
@@ -14,49 +16,41 @@ HEIGHT = 800
 IMAGES = []
 
 for name, val in {
-    'grass_01': 0,
-    'grass_02': 1,
-    'dry ground_01': 2,
-    'concrete_01': 3,
-    'concrete_02': 4,
-    'grass_01_rock': 0,
-    'grass_02_rock': 1,
-    'grass_01_mineAT_01': 0,
-    'grass_01_mineAP_02': 0,
-    'grass_01_mineAP_03': 0,
-    'grass_01_mineAT_04': 0,
-    'grass_01_mineADM': 0,
-    'grass_02_mineAT_01': 1,
-    'grass_02_mineAP_02': 1,
-    'grass_02_mineAP_03': 1,
-    'grass_02_mineADM': 1,
+    'grass': 0,
+    'dry ground': 1,
+    'concrete': 2,
     'mud_01': 0,
     'mud_02': 1,
-    'dry ground_01_mineADM': 2,
-    'dry ground_01_mineAP_02': 2,
-    'dry ground_01_mineAP_03': 2,
-    'dry ground_01_mineAT_01': 2,
-    'dry ground_01_mineAT_04': 2,
-    'dry ground_01_rock_01': 2,
-    'dry ground_01_rock_02': 2,
-    'concrete_01_mineAT_01': 3,
-    'concrete_01_mineAP_02': 3,
-    'concrete_01_mineAP_03': 3,
-    'concrete_01_mineAT_04': 3,
-    'concrete_01_mineADM': 3,
-    'concrete_02_mineAT_01': 4,
-    'concrete_02_mineAP_02': 4,
-    'concrete_02_mineAP_03': 4,
-    'concrete_02_mineADM': 4,
+    'grass_mineAT_01': 0,
+    'grass_mineAP_02': 0,
+    'grass_mineAP_03': 0,
+    'grass_mineAT_04': 0,
+    'grass_mineADM': 0,
+    'dry ground_mineADM': 1,
+    'dry ground_mineAP_02': 1,
+    'dry ground_mineAP_03': 1,
+    'dry ground_mineAT_01': 1,
+    'dry ground_mineAT_04': 1,
+    'concrete_mineAT_01': 2,
+    'concrete_mineAP_02': 2,
+    'concrete_mineAP_03': 2,
+    'concrete_mineAT_04': 2,
+    'concrete_mineADM': 2,
+    'concrete_rock_01': 2,
+    'concrete_rock_02': 2,
+    'grass_rock_01': 0,
+    'grass_rock_02': 0,
+    'dry ground_rock_01': 1,
+    'dry ground_rock_02': 1,
 }.items():
     folder_name = name.split('_')[0]
+    path = f'{main_path}/images/Tiles/{folder_name} Tiles'
     IMAGES.append(
         Image(
             val,
             name,
-            pg.image.load(
-                f'{main_path}/images/Tiles/{folder_name} Tiles/{name}.png'
-            ),
+            f'{path}/big/{name}.png',
+            pg.image.load(f'{path}/small/{name}.png'),
             None if 'mine' not in name else (
                 'AP' if 'AP' in name else (
                     'AT' if 'AT' in name else 'ADM'
@@ -89,7 +83,35 @@ for name in [
         f'{main_path}/images/agent/agent_kaboom/{name}.png'
     ))
 
-ROCK_INDEXES = (5, 6, 23, 24)
+ROCK_INDEXES = ( 20, 21, 22, 23, 24, 25)
 
-with open(f'{main_path}/src/machine_learning/decision_tree.pkl', 'rb') as file:
-    TREE_ROOT = pickle.load(file)
+try:
+    with open(f'{main_path}/src/machine_learning/decision_tree/decision_tree.pkl', 'rb') as file:
+        TREE_ROOT = pickle.load(file)
+
+except FileNotFoundError:
+    print('Decision tree not detected\nInitializing...')
+
+    from machine_learning.decision_tree.decision_tree import main
+    main()
+
+    with open(f'{main_path}/src/machine_learning/decision_tree/decision_tree.pkl', 'rb') as file:
+        TREE_ROOT = pickle.load(file)
+
+try:
+    checkpoint = torch.load(f'{main_path}/src/machine_learning/neural_network/mine_recognizer.model')
+    model = Net(num_classes=2)
+    model.load_state_dict(checkpoint)
+    model.eval()
+
+except FileNotFoundError:
+    print('Neural network not detected\nInitializing...')
+
+    from machine_learning.neural_network.learning import main
+    main()
+
+    checkpoint = torch.load(f'{main_path}/src/machine_learning/neural_network/mine_recognizer.model')
+
+    model = Net(num_classes=2)
+    model.load_state_dict(checkpoint)
+    model.eval()
diff --git a/src/environment.py b/src/environment.py
index c313719..ff03118 100644
--- a/src/environment.py
+++ b/src/environment.py
@@ -10,7 +10,7 @@ def generate_field() -> List[List[Tile]]:
     tiles_list = tiles_factory.get_tiles_list()
     return [choices(
         tiles_list,
-        weights=[20, 20] + [4 for _ in range(5)] + [10 for _ in range(9)] + [3 for _ in range(18)],
+        weights=[20, 10, 10, 5, 5] + [4 for _ in range(5)] + [3 for _ in range(10)] + [2 for _ in range(6)],
         k=10)
         for _ in range(10)
     ]
diff --git a/src/image.py b/src/image.py
index 0bfb02a..0b37fc8 100644
--- a/src/image.py
+++ b/src/image.py
@@ -4,8 +4,9 @@ from pygame import image
 
 
 class Image:
-    def __init__(self, parent: int, name: str, img: image, mine_type: Union[str, None]):
+    def __init__(self, parent: int, name: str, path: str, img: image, mine_type: Union[str, None]):
         self.parent = parent
+        self.name = name
+        self.path = path
         self.img = img
         self.mine_type = mine_type
-        self.name = name
diff --git a/src/machine_learning/decision_tree.pkl b/src/machine_learning/decision_tree.pkl
deleted file mode 100644
index 3604db7..0000000
Binary files a/src/machine_learning/decision_tree.pkl and /dev/null differ
diff --git a/src/machine_learning/decision_tree/__init__.py b/src/machine_learning/decision_tree/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/machine_learning/data.txt b/src/machine_learning/decision_tree/data.txt
similarity index 100%
rename from src/machine_learning/data.txt
rename to src/machine_learning/decision_tree/data.txt
diff --git a/src/machine_learning/data_set.py b/src/machine_learning/decision_tree/data_set.py
similarity index 96%
rename from src/machine_learning/data_set.py
rename to src/machine_learning/decision_tree/data_set.py
index a4b7034..440562a 100644
--- a/src/machine_learning/data_set.py
+++ b/src/machine_learning/decision_tree/data_set.py
@@ -1,10 +1,9 @@
-import os
 import json
 from typing import List
 from itertools import product
 
+from machine_learning.decision_tree.helpers import path
 
-data_path = os.path.dirname(os.path.abspath(__file__))
 
 visibility = ('bad', 'medium', 'good')
 stability = ('unstable', 'stable')
@@ -67,7 +66,7 @@ def main():
         json.dump(data_set, outfile)
 
 
-with open(f'{data_path}/data.txt', 'r') as f:
+with open(f'{path}/data.txt', 'r') as f:
     file_data = f.read()
     json_data = json.loads(file_data)
 
diff --git a/src/machine_learning/decision_tree.py b/src/machine_learning/decision_tree/decision_tree.py
similarity index 92%
rename from src/machine_learning/decision_tree.py
rename to src/machine_learning/decision_tree/decision_tree.py
index 2da317d..5989137 100644
--- a/src/machine_learning/decision_tree.py
+++ b/src/machine_learning/decision_tree/decision_tree.py
@@ -5,7 +5,8 @@ from collections import Counter
 
 from anytree import Node, RenderTree
 
-from machine_learning.data_set import training_set, test_set, attributes as attribs
+from machine_learning.decision_tree.helpers import path
+from machine_learning.decision_tree.data_set import training_set, test_set, attributes as attribs
 
 
 def calculate_entropy(p: int, n: int) -> float:
@@ -84,7 +85,7 @@ def main():
             score += 1
 
     print(f'Accuracy: {score/len(test_set)}')
-    with open('decision_tree.pkl', 'wb') as file:
+    with open(f'{path}/decision_tree.pkl', 'wb') as file:
         pickle.dump(tree_root, file, pickle.HIGHEST_PROTOCOL)
 
 
diff --git a/src/machine_learning/helpers.py b/src/machine_learning/decision_tree/helpers.py
similarity index 84%
rename from src/machine_learning/helpers.py
rename to src/machine_learning/decision_tree/helpers.py
index b7ab0df..a31c2e0 100644
--- a/src/machine_learning/helpers.py
+++ b/src/machine_learning/decision_tree/helpers.py
@@ -1,3 +1,4 @@
+import os
 from tile import Tile
 
 
@@ -10,3 +11,6 @@ def get_dataset_from_tile(tile: Tile):
         'mine_type': tile.mine.mine_type,
         'pressure_gt_two': True if tile.mine.pressure > 2 else False
     }
+
+
+path = os.path.dirname(os.path.abspath(__file__))
diff --git a/src/machine_learning/neural_network/__init__.py b/src/machine_learning/neural_network/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/machine_learning/neural_network/helpers.py b/src/machine_learning/neural_network/helpers.py
new file mode 100644
index 0000000..1fc7869
--- /dev/null
+++ b/src/machine_learning/neural_network/helpers.py
@@ -0,0 +1,24 @@
+import os
+
+from torchvision.transforms import transforms
+
+
+main_path = os.path.dirname(os.path.abspath(__file__))
+
+img_main_path = os.path.dirname(main_path)
+for i in range(2):
+    img_main_path = os.path.dirname(img_main_path)
+
+img_main_path = os.path.join(img_main_path, 'images/learning')
+
+train_path = os.path.join(img_main_path, 'training')
+test_path = os.path.join(img_main_path, 'test')
+prediction_path = os.path.join(img_main_path, 'prediction')
+
+transformer = transforms.Compose([
+    transforms.Resize((150, 150)),
+    transforms.RandomHorizontalFlip(),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5],
+                         [0.5, 0.5, 0.5])
+])
diff --git a/src/machine_learning/neural_network/learning.py b/src/machine_learning/neural_network/learning.py
new file mode 100644
index 0000000..0d4a71f
--- /dev/null
+++ b/src/machine_learning/neural_network/learning.py
@@ -0,0 +1,108 @@
+import os
+
+import torch
+import torchvision
+from PIL import Image
+import torch.nn as nn
+from torch.optim import Adam
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+
+from machine_learning.neural_network.net import Net
+from machine_learning.neural_network.helpers import main_path, train_path, test_path, prediction_path, transformer
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+classes = ['mine', 'rock']
+
+
+def train(dataloader, model: Net, optimizer: Adam, loss_fn: nn.CrossEntropyLoss):
+    size = len(dataloader.dataset)
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+
+        pred = model(X.float())
+        loss = loss_fn(pred, y)
+        
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if batch % 5 == 0:
+            loss, current = loss.item(), batch * len(X)
+            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
+
+
+def test(dataloader, model: Net, loss_fn: nn.CrossEntropyLoss):
+    size = len(dataloader.dataset)
+    model.eval()
+    test_loss, correct = 0, 0
+    
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X.float())
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+            
+    test_loss /= size
+    correct /= size
+    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
+
+
+def prediction(img_path, model: Net):
+    image = Image.open(img_path).convert('RGB')
+    image_tensor = transformer(image).float()
+    image_tensor = image_tensor.unsqueeze_(0)
+    
+    if torch.cuda.is_available():
+        image_tensor.cuda()
+    input = Variable(image_tensor)
+    output = model(input)
+    
+    index = output.data.numpy().argmax()
+    pred = classes[index]
+    return pred
+
+
+def test_prediction_set():
+    checkpoint = torch.load(f'{main_path}/mine_recognizer.model')
+    model = Net(num_classes=2)
+    model.load_state_dict(checkpoint)
+    model.eval()
+
+    pred_dict = {}
+
+    for file in os.listdir(prediction_path):
+        pred_dict[file[file.rfind('/') + 1:]] = prediction(f'{prediction_path}/{file}', model)
+
+    print(pred_dict)
+
+
+def main():
+    num_epochs = 50
+
+    train_loader = DataLoader(
+        torchvision.datasets.ImageFolder(train_path, transform=transformer),
+        batch_size=64, shuffle=True
+    )
+    test_loader = DataLoader(
+        torchvision.datasets.ImageFolder(test_path, transform=transformer),
+        batch_size=32, shuffle=True
+    )
+
+    model = Net(2).to(device)
+    optimizer = Adam(model.parameters(), lr=1e-3, weight_decay=0.0001)
+    loss_fn = nn.CrossEntropyLoss()
+
+    for t in range(num_epochs):
+        print(f"Epoch {t + 1}\n-------------------------------")
+        train(train_loader, model, optimizer, loss_fn)
+        test(test_loader, model, loss_fn)
+    print("Done!")
+    torch.save(model.state_dict(), f'{main_path}/mine_recognizer.model')
+    test_prediction_set()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/machine_learning/neural_network/mine_recognizer.model b/src/machine_learning/neural_network/mine_recognizer.model
new file mode 100644
index 0000000..43d9f92
Binary files /dev/null and b/src/machine_learning/neural_network/mine_recognizer.model differ
diff --git a/src/machine_learning/neural_network/net.py b/src/machine_learning/neural_network/net.py
new file mode 100644
index 0000000..7913b5c
--- /dev/null
+++ b/src/machine_learning/neural_network/net.py
@@ -0,0 +1,39 @@
+import torch.nn as nn
+
+
+class Net(nn.Module):
+    def __init__(self, num_classes=6):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
+        self.bn1 = nn.BatchNorm2d(num_features=12)
+        self.relu1 = nn.ReLU()
+
+        self.pool = nn.MaxPool2d(kernel_size=2)
+
+        self.conv2 = nn.Conv2d(in_channels=12, out_channels=20, kernel_size=3, stride=1, padding=1)
+        self.relu2 = nn.ReLU()
+
+        self.conv3 = nn.Conv2d(in_channels=20, out_channels=32, kernel_size=3, stride=1, padding=1)
+        self.bn3 = nn.BatchNorm2d(num_features=32)
+        self.relu3 = nn.ReLU()
+
+        self.fc = nn.Linear(in_features=75 * 75 * 32, out_features=num_classes)
+
+    def forward(self, input):
+        output = self.conv1(input)
+        output = self.bn1(output)
+        output = self.relu1(output)
+
+        output = self.pool(output)
+
+        output = self.conv2(output)
+        output = self.relu2(output)
+
+        output = self.conv3(output)
+        output = self.bn3(output)
+        output = self.relu3(output)
+
+        output = output.view(-1, 32 * 75 * 75)
+        output = self.fc(output)
+
+        return output
diff --git a/src/main.py b/src/main.py
index cce13d1..99d0ce4 100644
--- a/src/main.py
+++ b/src/main.py
@@ -7,8 +7,8 @@ from tilesFactory import TilesFactory
 from const import ICON, IMAGES, TREE_ROOT
 from src.search_algoritms.a_star import a_star
 from search_algoritms.BFS import breadth_first_search
-from machine_learning.decision_tree import get_decision
-from machine_learning.helpers import get_dataset_from_tile
+from machine_learning.decision_tree.decision_tree import get_decision
+from machine_learning.decision_tree.helpers import get_dataset_from_tile
 
 
 def handle_keys(env: Environment, agent: Agent, game_ui: GameUi, factory: TilesFactory):
@@ -63,12 +63,6 @@ def main():
 
     running = True
 
-    # ruchy agenta:
-    # 0 - up
-    # 1 - right
-    # 2 - down
-    # 3 - left
-
     while running:
         for event in pg.fastevent.get():
             if event.type == pg.QUIT:
@@ -76,23 +70,23 @@ def main():
             elif event.type == pg.KEYDOWN:
                 if event.key == pg.K_t:
                     print('Starting to clear the sector')
-                    while env.mine_count:
-                        print('-' * 20)
-                        # path, actions = breadth_first_search(env.field, agent.x, agent.y, agent.direction)
-                        goal = breadth_first_search(env.field, agent.x, agent.y, agent.direction, True)
-                        path, actions = a_star(env.field, agent.x, agent.y, agent.direction, goal)
+                    # while env.mine_count:
+                    print('-' * 20)
 
-                        if not path and not env.field[agent.y][agent.x].mine:
-                            print('Unable to find path, rocks are in the way')
-                            break
-                        print(f'Path{path}')
+                    goal = breadth_first_search(env.field, agent.x, agent.y, agent.direction, True)
+                    path, actions = a_star(env.field, agent.x, agent.y, agent.direction, goal)
 
-                        print(f'Actions:{actions}')
-                        for action in actions:
-                            pg.fastevent.post(pg.event.Event(pg.KEYDOWN, {'key': action}))
+                    if not path and not env.field[agent.y][agent.x].mine:
+                        print('Unable to find path, rocks are in the way')
+                        break
+                    print(f'Path{path}')
 
-                        handle_keys(env, agent, game_ui, factory)
-                        pg.fastevent.post(pg.event.Event(pg.KEYDOWN, {'key': pg.K_SPACE}))
+                    print(f'Actions:{actions}')
+                    for action in actions:
+                        pg.fastevent.post(pg.event.Event(pg.KEYDOWN, {'key': action}))
+
+                    handle_keys(env, agent, game_ui, factory)
+                    pg.fastevent.post(pg.event.Event(pg.KEYDOWN, {'key': pg.K_SPACE}))
 
                     print('Sector clear')
                 else:
diff --git a/src/search_algoritms/BFS.py b/src/search_algoritms/BFS.py
index fed2c0a..e387bce 100644
--- a/src/search_algoritms/BFS.py
+++ b/src/search_algoritms/BFS.py
@@ -2,8 +2,10 @@ import queue
 from typing import List
 
 from src.tile import Tile
+from const import model, IMAGES
 from search_algoritms.node import Node
 from .helpers import successor, get_path_actions
+from machine_learning.neural_network.learning import prediction
 
 
 def breadth_first_search(
@@ -20,7 +22,8 @@ def breadth_first_search(
     while not node_queue.empty():
         node = node_queue.get()
 
-        if field[node.y][node.x].mine:
+        img_path = IMAGES[field[node.y][node.x].number].path
+        if field[node.y][node.x].occupied and prediction(img_path, model) == 'mine':
             return get_path_actions(node) if not a_star else node.x, node.y
 
         explored.append(node)
diff --git a/src/search_algoritms/a_star.py b/src/search_algoritms/a_star.py
index 766261b..1a7c299 100644
--- a/src/search_algoritms/a_star.py
+++ b/src/search_algoritms/a_star.py
@@ -14,7 +14,7 @@ def a_star(field: List[List[Tile]], start_x: int, start_y: int, start_direction:
 
         if (node.x, node.y) == goal:
             return get_path_actions_a_star(node, field)
-        
+
         explored.append(node)
         for x, y, direction, action in successor(field, node.x, node.y, node.direction):
             neighbour_node = Node(x, y, direction, node, action)
diff --git a/src/search_algoritms/helpers.py b/src/search_algoritms/helpers.py
index acb3dbe..117c2d1 100644
--- a/src/search_algoritms/helpers.py
+++ b/src/search_algoritms/helpers.py
@@ -3,8 +3,9 @@ from typing import List, Tuple
 import pygame as pg
 
 from src.tile import Tile
-from const import ROCK_INDEXES
-from search_algoritms.node import Node
+from src.const import IMAGES, model
+from src.search_algoritms.node import Node
+from machine_learning.neural_network.learning import prediction
 
 
 def get_path_actions(node: Node):
@@ -31,10 +32,19 @@ def successor(field: List[List[Tile]], x: int, y: int, direction: int):
     neighbours.append((x, y, (direction - 1) % 4, pg.K_a))
     neighbours.append((x, y, (direction + 1) % 4, pg.K_d))
 
-    if coord == 'x' and 0 <= x + shift <= 9 and field[y][x + shift].number not in ROCK_INDEXES:
-        neighbours.append((x + shift, y, direction, pg.K_w))
-    elif coord == 'y' and 0 <= y + shift <= 9 and field[y + shift][x].number not in ROCK_INDEXES:
-        neighbours.append((x, y + shift, direction, pg.K_w))
+    if coord == 'x' and 0 <= x + shift <= 9:
+        img_path = IMAGES[field[y][x + shift].number].path
+        impassable = True if field[y][x + shift].occupied and prediction(img_path, model) == 'rock' else False
+
+        if not impassable:
+            neighbours.append((x + shift, y, direction, pg.K_w))
+
+    elif coord == 'y' and 0 <= y + shift <= 9:
+        img_path = IMAGES[field[y + shift][x].number].path
+        impassable = True if field[y + shift][x].occupied and prediction(img_path, model) == 'rock' else False
+
+        if not impassable:
+            neighbours.append((x, y + shift, direction, pg.K_w))
 
     return neighbours
 
diff --git a/src/tile.py b/src/tile.py
index 2280ac7..9bb7982 100644
--- a/src/tile.py
+++ b/src/tile.py
@@ -3,6 +3,7 @@ from typing import Union
 from ap_mine import APMine
 from at_mine import ATMine
 from adm_mine import ADMMine
+from const import ROCK_INDEXES
 
 
 class Tile:
@@ -21,3 +22,4 @@ class Tile:
         self.visibility = visibility
         self.stability = stability
         self.ground = ground
+        self.occupied = True if mine or number in ROCK_INDEXES else False
diff --git a/src/tilesFactory.py b/src/tilesFactory.py
index d52ba55..bac27f2 100644
--- a/src/tilesFactory.py
+++ b/src/tilesFactory.py
@@ -4,7 +4,7 @@ from importlib import import_module
 
 from tile import Tile
 from const import IMAGES
-from machine_learning.data_set import visibility, stability, armed
+from machine_learning.decision_tree.data_set import visibility, stability, armed
 
 
 class TilesFactory:
@@ -42,7 +42,7 @@ class TilesFactory:
 											)
 	
 	def get_tiles_list(self) -> List[Tile]:
-		return [self.create_tile(i) for i in range(34)]
+		return [self.create_tile(i) for i in range(26)]
 	
 	def get_random_value(self, values):
 		return choice(values)